Artificial intelligence planning systems attempting to achieve human-like performance typically bring to bear a wealth of real-world knowledge in order to select actions consistent with the system's goals and its assessment of the state of its environment. Unfortunately, as machine reasoning systems become larger and more general, they frequently become correspondingly slower and hence less effective at their intended task. Meanwhile, most human actors can deal competently with quite complex environments without compelling evidence that they plan by relying principally upon (or even understanding) formal reasoning and planning techniques such as resolution theorem proving, dynamic programming, and backward chaining. We suggest that humans can plan and replan so quickly because of two important principles: (a) their internal represention of the world is well suited to the planning problems they solve, and (b) their plans have much less depth than most powerful machine reasoning systems. A good substitute for deep planning may be a "broad but shallow" planning strategy that generates plans terminated in parameterized action sequences ("behaviors") which are chunked at a relatively high level of abstraction, combined with a context-dependent salience measure that differentially cues plan fragments or "behaviors" to propose themselves as candidates during time-critical planning operations.